Graph-based Robot Localization Using a Graph Neural Network with a Floor Camera and a Feature Rich Industrial Floor

TL;DR

This paper introduces a novel graph neural network-based localization method that uses floor features for highly accurate and efficient robot positioning, overcoming traditional limitations and solving the kidnapped robot problem.

Contribution

The work presents a new graph-based localization framework utilizing GCNs and floor features, achieving high accuracy and robustness without complex filtering.

Findings

Localization error of 0.64cm achieved

Effectively addresses kidnapped robot problem

Outperforms traditional feature comparison methods

Abstract

Accurate localization represents a fundamental challenge in robotic navigation. Traditional methodologies, such as Lidar or QR-code based systems, suffer from inherent scalability and adaptability con straints, particularly in complex environments. In this work, we propose an innovative localization framework that harnesses flooring characteris tics by employing graph-based representations and Graph Convolutional Networks (GCNs). Our method uses graphs to represent floor features, which helps localize the robot more accurately (0.64cm error) and more efficiently than comparing individual image features. Additionally, this approach successfully addresses the kidnapped robot problem in every frame without requiring complex filtering processes. These advancements open up new possibilities for robotic navigation in diverse environments.